Governed data access layer
Connect supported data sources and expose only the data you want agents to reach through a controlled layer between agents and your databases.
Pylar
RivendicaPylar is a governed data access layer for AI agents that lets teams expose SQL views as MCP tools instead of direct database access. It is built for connecting structured data sources to agent builders with controlled permissions and observability.
Overview
Pylar is a governed data access layer for AI agents. It sits between agents and your databases so you can decide what data is exposed, shape that data with SQL views, and publish it as MCP tools for agent builders to use.
The product centers on controlled access rather than direct database access. The homepage shows SQL view creation, MCP tool publishing, and observability features for monitoring how agent-facing tools perform after deployment.
Core features
View-based governance
Create SQL views that act as the only access level for agents, allowing filtering of sensitive data, row-level security, and joins across databases.
MCP tool generation
Turn views into MCP tools, either from natural language or manual configuration, and build multiple tools from a single view.
Centralized publishing
Publish tools once and reuse a single MCP server URL and token across connected agent builders, with updates reflected automatically.
Evals and observability
Track success rates, latency, costs, errors, and query patterns to understand how tools behave in production.
Broad connector and builder coverage
See example integrations and connected builders in the product flow, including BigQuery, Postgres, Snowflake, HubSpot, Stripe, Zendesk, Claude Desktop, Cursor, Windsurf, VS Code, LangGraph, OpenAI Platform, Zapier, Make, and n8n.
Practical use cases
Support and customer operations agents
Define a customer support or customer health view, then publish MCP tools that let agents fetch safe, structured customer information by email or ID.
Cross-system analytics access
Join data across warehouse tables and operational systems into a governed view so agents can answer questions from one curated source instead of raw tables.
Multi-builder agent deployment
Create tools for support tickets, error codes, product events, or revenue data and connect them to builders such as Cursor, Claude Desktop, or n8n.
Centralized tool maintenance
Use the publish-once workflow to update a SQL view or tool definition in Pylar and push changes to connected builders without redeploying each agent separately.
Production monitoring and refinement
Monitor how tools are being used in production with evals, then inspect latency, errors, and query patterns to refine the exposed data surface.
Pros and Cons
Pros
- Lets teams expose only governed SQL views instead of raw tables.
- Supports turning views into MCP tools for agent builders.
- Shows a publish-once workflow where tool changes propagate to connected builders.
- Includes observability for tool usage, latency, errors, and cost trends.
- Appears to support multiple data sources and multiple agent builders from one control layer.
Cons
- Pricing details are not available in the collected sources.
- The available information is limited to the homepage, so some setup, security, and deployment details remain unconfirmed.
FAQ
What does Pylar do?
Pylar sits between AI agents and your data sources. You connect sources, define governed SQL views, and publish MCP tools that agents can call through those views.
What data sources and agent builders does it connect to?
The source shows integrations or connections for BigQuery, Postgres, Snowflake, HubSpot, Stripe, and Zendesk. It also shows connected builders such as Claude Desktop, Cursor, Windsurf, VS Code, LangGraph, OpenAI Platform, Zapier, Make, and n8n.
Can teams create tools without writing a separate API?
Yes. The homepage shows a flow where you create MCP tools from views using natural language or manual configuration, then publish them for agent use.
How does Pylar govern access to data?
The homepage emphasizes that agents query through SQL views rather than raw tables, and that you can filter sensitive data and implement row-level security.
Is pricing publicly documented?
The pricing page was not available in the collected sources, so pricing, plan shape, and any usage limits are not confirmed here.
Quick Facts
- Category
- AI agent data access
- Primary workflow
- Govern SQL views, create MCP tools, publish to builders
- Source domain
- pylar.ai
- Data sources shown
- BigQuery, Postgres, Snowflake, HubSpot, Stripe, Zendesk
- Builders shown
- Claude Desktop, Cursor, Windsurf, VS Code, LangGraph, OpenAI Platform, Zapier, Make, n8n
- Pricing
- Not available in the collected sources
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